Does thinning intensity affect the tracheid dimensions of Norway spruce?
Bibliographic record
Abstract
The effect of thinning intensity on the growth rate and tracheid dimensions of Norway spruce (Picea abies (L.) Karst) was studied in two long-term thinning experiments (Heinola and Punkaharju) in southeastern Finland. The stand age was 86 and 67 years in Heinola and Punkaharju, respectively. Thinning intensities in this study were lower and higher than recommended in the 1960s for forestry practice in Finland. An increase in tree growth rate (31% in Heinola and 37% in Punkaharju) caused by the high thinning intensity resulted in slightly shorter tracheids (9% in Heinola and 4% in Punkaharju) than with the low thinning intensity. Increased growth rate had no pronounced effect on tracheid cell wall thickness and lumen diameter. A faster growth rate slightly decreased the average cell wall thickness of an annual ring, but the changes in average lumen diameter were small. The effect of thinning intensity was similar in earlywood and latewood. Variation in fiber properties between and within individual trees and annual rings was large. In conclusion, the current thinning intensities used in Finnish forestry practice enhance growth rate but have a rather small effect on tracheid dimensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".